M2PP: Effective Path Planning Based on Multi-Phase Particle Swarm Optimization and Multi-Scenario Adaptative DWA

Abstract

To address the challenge of autonomous navigation for mobile robots in complex environments, many recent path planning methods employ a two-layer motion framework that integrates both global and local planning. However, global planning algorithms based on evolutionary algorithms often yield suboptimal paths, primarily due to premature convergence and wasted iterations in infeasible regions. Meanwhile, local planning algorithms, particularly the dynamic window approach (DWA) and its variants, are frequently tailored to specific scenarios, limiting their generalizability. To address these issues, this paper proposes an effective path planning method, namely M2PP. The global planning algorithm in M2PP enhances particle swarm optimization (PSO) by introducing multiple new search phases, creating a multi-phase PSO that can quickly identify feasible global paths and thoroughly explore the sampling space. To effectively handle multiple scenarios in a more generalized manner, the local planning component, multi-scenario adaptative DWA, integrates two novel terms into its cost function, enhancing both static and dynamic obstacle avoidance. Extensive simulations demonstrate that multi-phase PSO exhibits strong performance and robustness, especially in highly complex scenarios, and multi-scenario adaptative DWA can generate safe and efficient local trajectories in hazardous conditions. Additionally, experiments deploying the M2PP method on a real-world mobile robot further confirm its feasibility.